Wednesday, November 06, 2019

Simulating Urban Patterns of Life: A Geo-Social Data Generation Framework

At the ACM SIGSPATIAL'19 conference, Joon-Seok Kim, Hamdi Kavak, Umar Manzoor, Dieter Pfoser, Carola Wenk, Andreas Züfle and myself have a paper entitled "Simulating Urban Patterns of Life: A Geo-Social Data Generation Framework." The general idea behind the paper is that while trajectory data is being used to capture human mobility in many applications (e.g. traffic prediction, ride-sharing applications), the use of real-world trajectory data raises serious concerns with respect to the privacy of users who contribute such information. 

To overcome privacy concerns we have created a geo-social data generator by utilizing agent-based modeling. The notion behind this generator is to allow users to develop and customize the logic of agent behaviors for different applications domains (e.g. commuting around a city). Once the basic model is created, the simulation can then be run and  geo-social data is generated which can then be used as a substitute to real-world trajectory data to study human mobility. If you wish to find out more about this paper, below is the abstract to the paper, along with some figures of the framework architecture and a link to the paper. Further supplementary materials including a demo video (which is also below) and sample data can be found at:

Data generators have been heavily used in creating massive trajectory datasets to address common challenges of real-world datasets, including privacy, cost of data collection, and data quality. However, such generators often overlook social and physiological characteristics of individuals and as such their results are often limited to simple movement patterns. To address these shortcomings, we propose an agent-based simulation framework that facilitates the development of behavioral models in which agents correspond to individuals that act based on personal preferences, goals, and needs within a realistic geographical environment. Researchers can use a drag-and-drop interface to design and control their own world including the geospatial and social (i.e. geo-social) properties. The framework is capable of generating and streaming very large data that captures the basic patterns of life in urban areas. Streaming data from the simulation can be accessed in real time through a dedicated API. 
Keywords: Agent-based simulation, trajectory data, data generator, spatial network, human behavior.
Causality in human behavior

Architecture of framework

Layout of model builder and sample model

Full Reference:
Kim, J-S., Kavak, H., Manzoor, U., Crooks, A.T., Pfoser, D., Wenk C. and Z├╝fle, A (2019), Simulating Urban Patterns of Life: A Geo-Social Data Generation Framework, in Banaei-Kashani, F., Trajcevski, G., Güting, R.H., Kulik, L. and Newsam, S. (eds.), Proceedings of the 27th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019), Chicago, IL. (pdf)

Tuesday, November 05, 2019

New Paper: Assessing the Placeness of Locations through User-contributed Content

In the past we have written about how one can use crowdsourced data to gain a collective sense of place from Twitter contributions and also from corresponding Wikipedia entries (e.g. here). In a new paper with Xiaoyi Yuan, we extend this work to explore how user-contributed data can be used to explore if urban places are becoming inauthentic due to urban commodification and standardization by chain stores such as restaurants. To this end, at the at 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI) we have a paper entitled: "Assessing the Placeness of Locations through User-contributed Content"

In the paper we attempt to understand the relationship between restaurants and urban identities via user-contributed content. We extracted and analyzed information from over 3 million Yelp reviews from 37,000 restaurants using a Convolutional Neural Network (CNN) model in order to study places from the bottom up. Specifically we were interested to what extent cities share similarities or differences in their Yelp restaurant reviews. Furthermore, we wanted to explore how opinion aspects (i.e. what reviewers care about the most) are mentioned differently in urban chain and independent restaurants. Through the analysis of the Yelp reviews we find that online geo-tagged text data is fruitful for understanding places and aspect-based sentiment analysis helps us understand the large volumes of text. Not only did we discover that cities show homogeneity in terms of restaurant reviews, but for chain restaurants, “location” often emphasizes the differences between different stores of the same chain whereas for independent restaurant reviews, the aspect “location” reflects the characteristics of the places the restaurants are situated. If this is of interest to you, below we provide the abstract to the paper, along with some of the key findings and a link to the paper.

Previous research has argued that urban places are becoming “placeless” and inauthentic. Many local policies have also proposed to encourage more independent stores in order to restore urban identity. Others argue, however, that chain stores provide affordable merchandise and different locations of the same chain may have different meanings to an individual. The research presented in this paper uses a Convolutional Neural Networks model to extract opinion aspects from more than 3 million user-contributed Yelp restaurant reviews. The results show high homogeneity among cities in terms of the average proportions of aspects in restaurant reviews. In addition, for fast food chains, “location” is the only aspect category reviewed proportionally higher than independent fast food restaurants. An analysis of the co-occurrences of “location” indicates that the identity of chain restaurants stems from the comparison between the same chain of different locations whereas the identity of the independent restaurants is more diverse, implying the intricacies of placeness of urban stores. This research demonstrates that fine-grained sentiment analysis (i.e., opinion aspect extraction and analysis) with geo-tagged text data is fruitful for studying nuanced place perceptions on a large scale.
KEYWORDS: Urban Places, Convolutional Neural Networks, Aspect-based Sentiment Analysis
Figure 1: Illustration of an example of a CNN layer.
Figure 3: Mapping restaurants in NV, AZ, PA, NC, WI, IL. Not all cities are shown in each state. Only cities have data that accounts for the majority of the restaurants in that state are mapped, for the sake of visual clarity.
Figure 6: Average proportions of aspect categories for chain and independent fast food restaurants for two kinds of cuisine (American, Mexican) in Las Vegas, Phoenix, and Charlotte, normalized by dividing the mean for comparison.
Yuan X. and Crooks A.T. (2019), Assessing the Placeness of Locations through User-contributed Content, in Gao, S., Newsam, S., Zhao, L., Lunga, D., Hu, Y., Martins, B., Zhou, X. and Chen, F. (eds.), Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI), Chicago, IL. pp. 15-23. (pdf)